The Instacart Online Grocery Shopping Dataset 2017

---
title: "dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
```


```{r}
library(tidyverse)
library(p8105.datasets)
library(plotly)

```

The Instacart Online Grocery Shopping Dataset 2017

```{r}
data(instacart)
instacart =
  instacart %>% 
  janitor::clean_names()
```




Column {data-width=650}
-----------------------------------------------------------------------

### Chart A

```{r}
plotA =
  instacart %>%
  count(aisle) %>% 
  filter(n > 10000) %>%
  mutate(
    aisle = fct_reorder(aisle,n)
    ) %>% #reorder/ranking
   mutate(
    stratify = case_when(
      n <=  20000 ~ "under 20000",
      (n > 20000)&(n < 40000) ~ "20000 to 40000",
      n > 40000      ~ "above 40000"
  )) %>% 
  ggplot(aes(x = aisle, y = n, fill = stratify)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(
    title = "The Number of Items Ordered in Each Aisle",
    x = "The number of items",
    y = "Aisle",
  )

ggplotly(plotA) 
```

Column {data-width=350}
-----------------------------------------------------------------------

### Chart B

```{r}
set.seed(1)
plotB =
  instacart %>%
  sample_n(1000) %>% 
  filter(aisle_id < 20) %>% 
  ggplot(aes(x = aisle, y = add_to_cart_order, fill = department)) +
  geom_boxplot() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))
 

ggplotly(plotB) 
```

### Chart C

```{r}
set.seed(1)
plotC =
  instacart %>%
  sample_n(1000) %>% 
  filter(order_dow == 1) %>% 
  ggplot(aes(x = order_number, y = order_hour_of_day, color = department)) +
  geom_point()
 

ggplotly(plotC) 
```